Make Your Tools Sparkle with Trust: The PICSE Framework for Trust in Software Tools
The day to day of a software engineer involves a variety of tasks. While many of these tasks are collaborative and completed as such, it is not always possible or feasible to engage with other engineers for task completion. Software tools, such as code generators and static analysis tools, aim to fill this gap by providing additional support for developers to effectively complete their tasks. With a steady stream of new tools that emerging to support software engineers, including a new breed of tools that rely on artificial intelligence, there are important questions we should aim to answer regarding the trust engineers can, and should, put into their software tools and what it means to build a trustworthy tool. In this paper, we present findings from an industry interview study conducted with 18 engineers across and external to the Microsoft organization. Based on these interviews, we introduce the PICSE (pronounced “pixie”) framework for trust in software tools to provide preliminary insights into factors that influence engineer trust in their software tools. We also discuss how the PICSE framework can be considered and applied in practice for designing and developing trustworthy software tools.
Fri 19 MayDisplayed time zone: Hobart change
13:45 - 15:15 | Software development toolsDEMO - Demonstrations / Technical Track / SEIP - Software Engineering in Practice / NIER - New Ideas and Emerging Results at Meeting Room 104 Chair(s): Xing Hu Zhejiang University | ||
13:45 15mTalk | Safe low-level code without overhead is practical Technical Track Pre-print | ||
14:00 15mTalk | Sibyl: Improving Software Engineering Tools with SMT Selection Technical Track Will Leeson University of Virgina, Matthew B Dwyer University of Virginia, Antonio Filieri AWS and Imperial College London Pre-print | ||
14:15 15mTalk | Make Your Tools Sparkle with Trust: The PICSE Framework for Trust in Software Tools SEIP - Software Engineering in Practice Brittany Johnson George Mason University, Christian Bird Microsoft Research, Denae Ford Microsoft Research, Nicole Forsgren Microsoft Research, Thomas Zimmermann Microsoft Research Pre-print | ||
14:30 15mTalk | CoCoSoDa: Effective Contrastive Learning for Code Search Technical Track Ensheng Shi Xi'an Jiaotong University, Wenchao Gu The Chinese University of Hong Kong, Yanlin Wang School of Software Engineering, Sun Yat-sen University, Lun Du Microsoft Research Asia, Hongyu Zhang The University of Newcastle, Shi Han Microsoft Research, Dongmei Zhang Microsoft Research, Hongbin Sun Xi'an Jiaotong University Pre-print | ||
14:45 7mTalk | Task Context: A Tool for Predicting Code Context Models for Software Development Tasks DEMO - Demonstrations Yifeng Wang Zhejiang University, Yuhang Lin Zhejiang University, Zhiyuan Wan Zhejiang University, Xiaohu Yang Zhejiang University Pre-print Media Attached | ||
14:52 7mTalk | Continuously Accelerating Research NIER - New Ideas and Emerging Results Sergey Mechtaev University College London, Jonathan Bell Northeastern University, Christopher Steven Timperley Carnegie Mellon University, Earl T. Barr University College London, Michael Hilton Carnegie Mellon University Pre-print | ||
15:00 7mTalk | An Alternative to Cells for Selective Execution of Data Science Pipelines NIER - New Ideas and Emerging Results Pre-print | ||
15:07 7mTalk | pytest-inline: An Inline Testing Tool for Python DEMO - Demonstrations Yu Liu University of Texas at Austin, Zachary Thurston Cornell University, Alan Han Cornell University, Pengyu Nie University of Texas at Austin, Milos Gligoric University of Texas at Austin, Owolabi Legunsen Cornell University | ||